enhance-prompts

# enhance-prompts

Analyze prompts for clarity, structure, examples, and output reliability.

## Differentiation from enhance-agent-prompts

| Skill | Focus | Use When |
|-------|-------|----------|
| `enhance-prompts` | Prompt quality (clarity, structure, examples) | General prompts, system prompts, templates |
| `enhance-agent-prompts` | Agent config (frontmatter, tools, model) | Agent files with YAML frontmatter |

## Workflow

1. **Discover** - Find prompt files (.md, .txt)
2. **Classify** - Detect prompt type from path/content
3. **Check** - Run pattern checks against knowledge below
4. **Filter** - Apply certainty filtering
5. **Report** - Generate markdown output
6. **Fix** - Apply auto-fixes if --fix flag present

---

## Prompt Engineering Knowledge Reference

### System Prompt Structure

Effective system prompts include: Role/Identity, Capabilities & Constraints, Instruction Priority, Output Format, Behavioral Directives, Examples, Error Handling.

**Minimal Template:**
```xml
<system>
You are [ROLE]. [PURPOSE].
Key constraints: [CONSTRAINTS]
Output format: [FORMAT]
When uncertain: [HANDLING]
</system>
```

### XML Tags (Claude-Specific)

Claude is fine-tuned for XML tags. Use: `<role>`, `<constraints>`, `<output_format>`, `<examples>`, `<instructions>`, `<context>`

```xml
<constraints>
- Maximum response length: 500 words
- Use only Python 3.10+ syntax
</constraints>
```

### Few-Shot Examples

- 2-5 examples is optimal (research-backed)
- Include edge cases and ensure format consistency
- Start zero-shot, add examples only if needed
- Show both good AND bad examples when relevant

### Chain-of-Thought (CoT)

| Use CoT | Don't Use CoT |
|---------|---------------|
| Complex multi-step reasoning | Simple factual questions |
| Math and logic problems | Classification tasks |
| Code debugging | When model has built-in reasoning |

**Key:** Modern models (Claude 4.x, o1/o3) perform CoT internally. "Think step by step" is redundant.

### Role Prompting

**Hel